{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f41bc2da",
   "metadata": {},
   "source": [
    "# 第四问代码部分\n",
    "**第四问分析流程**：\n",
    "1.提取原数据特征 --> 2.将数据分类，分为10个类 --> 3.从已经分类好的数据中提取出时间序列数据 --> 4.选用分类器对数据进行分类 --> 5.根据分类器得分，选择出随机森林、EM和K近邻分类器，下面对分类器进行参数优化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f125cc2",
   "metadata": {},
   "source": [
    "## 1 提取原数据特征"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1a229f",
   "metadata": {},
   "source": [
    "### 1.1 提取美的暖通数据特征 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2940b598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>上升剧烈的点</th>\n",
       "      <th>集中用电的点</th>\n",
       "      <th>下降剧烈的点</th>\n",
       "      <th>标准差</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017年04月28日</td>\n",
       "      <td>[8, 13, 9]</td>\n",
       "      <td>[17, 15, 10, 21, 16]</td>\n",
       "      <td>[22, 12, 23]</td>\n",
       "      <td>0.377648</td>\n",
       "      <td>7304.209375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017年04月29日</td>\n",
       "      <td>[13, 8, 7]</td>\n",
       "      <td>[15, 14, 10, 11, 9]</td>\n",
       "      <td>[17, 22, 12]</td>\n",
       "      <td>0.250236</td>\n",
       "      <td>4667.809458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017年04月30日</td>\n",
       "      <td>[8, 9, 13]</td>\n",
       "      <td>[17, 15, 16, 14, 10]</td>\n",
       "      <td>[17, 22, 23]</td>\n",
       "      <td>0.394272</td>\n",
       "      <td>6295.234333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017年04月28日</td>\n",
       "      <td>[8, 13, 9]</td>\n",
       "      <td>[17, 15, 10, 21, 16]</td>\n",
       "      <td>[22, 12, 23]</td>\n",
       "      <td>0.377648</td>\n",
       "      <td>7304.209375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017年04月29日</td>\n",
       "      <td>[13, 8, 7]</td>\n",
       "      <td>[15, 14, 10, 11, 9]</td>\n",
       "      <td>[17, 22, 12]</td>\n",
       "      <td>0.250236</td>\n",
       "      <td>4667.809458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2017年08月16日</td>\n",
       "      <td>[8, 9, 13]</td>\n",
       "      <td>[17, 11, 16, 14, 18]</td>\n",
       "      <td>[19, 12, 23]</td>\n",
       "      <td>0.378559</td>\n",
       "      <td>8323.797167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2017年08月17日</td>\n",
       "      <td>[8, 9, 13]</td>\n",
       "      <td>[17, 11, 15, 16, 18]</td>\n",
       "      <td>[21, 23, 12]</td>\n",
       "      <td>0.372487</td>\n",
       "      <td>8578.246250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2017年08月18日</td>\n",
       "      <td>[8, 13, 9]</td>\n",
       "      <td>[15, 17, 11, 16, 14]</td>\n",
       "      <td>[23, 21, 12]</td>\n",
       "      <td>0.376089</td>\n",
       "      <td>8848.040333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2017年08月19日</td>\n",
       "      <td>[8, 13, 9]</td>\n",
       "      <td>[14, 11, 18, 10, 19]</td>\n",
       "      <td>[15, 12, 23]</td>\n",
       "      <td>0.378183</td>\n",
       "      <td>8534.629250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2017年08月20日</td>\n",
       "      <td>[8, 9, 13]</td>\n",
       "      <td>[11, 10, 14, 12, 15]</td>\n",
       "      <td>[12, 15, 16]</td>\n",
       "      <td>0.317285</td>\n",
       "      <td>6984.272083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              日期      上升剧烈的点                集中用电的点        下降剧烈的点       标准差  \\\n",
       "0    2017年04月28日  [8, 13, 9]  [17, 15, 10, 21, 16]  [22, 12, 23]  0.377648   \n",
       "1    2017年04月29日  [13, 8, 7]   [15, 14, 10, 11, 9]  [17, 22, 12]  0.250236   \n",
       "2    2017年04月30日  [8, 9, 13]  [17, 15, 16, 14, 10]  [17, 22, 23]  0.394272   \n",
       "3    2017年04月28日  [8, 13, 9]  [17, 15, 10, 21, 16]  [22, 12, 23]  0.377648   \n",
       "4    2017年04月29日  [13, 8, 7]   [15, 14, 10, 11, 9]  [17, 22, 12]  0.250236   \n",
       "..           ...         ...                   ...           ...       ...   \n",
       "133  2017年08月16日  [8, 9, 13]  [17, 11, 16, 14, 18]  [19, 12, 23]  0.378559   \n",
       "134  2017年08月17日  [8, 9, 13]  [17, 11, 15, 16, 18]  [21, 23, 12]  0.372487   \n",
       "135  2017年08月18日  [8, 13, 9]  [15, 17, 11, 16, 14]  [23, 21, 12]  0.376089   \n",
       "136  2017年08月19日  [8, 13, 9]  [14, 11, 18, 10, 19]  [15, 12, 23]  0.378183   \n",
       "137  2017年08月20日  [8, 9, 13]  [11, 10, 14, 12, 15]  [12, 15, 16]  0.317285   \n",
       "\n",
       "              均值  \n",
       "0    7304.209375  \n",
       "1    4667.809458  \n",
       "2    6295.234333  \n",
       "3    7304.209375  \n",
       "4    4667.809458  \n",
       "..           ...  \n",
       "133  8323.797167  \n",
       "134  8578.246250  \n",
       "135  8848.040333  \n",
       "136  8534.629250  \n",
       "137  6984.272083  \n",
       "\n",
       "[138 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import re\n",
    "\n",
    "result=[]\n",
    "for d in range(4,9):\n",
    "    path = 'E:/中国软件杯2022/美的暖通/2017.'+str(d)  # 输入文件夹地址\n",
    "    files = list(os.listdir(path))  # 读入文件夹\n",
    "    for j in range(len(files)):\n",
    "        s='E:/中国软件杯2022/美的暖通/2017.'+str(d)+'/'+files[j]\n",
    "        data=pd.read_excel(s)\n",
    "        data=data.loc[:23,['小时','电量（kWh）']]\n",
    "        data=np.array(data)\n",
    "\n",
    "        # 对时间段重新编号\n",
    "        for i in range(len(data)):\n",
    "            data[i][0]=i+1\n",
    "\n",
    "        # 标准化电量\n",
    "        elec=data[:,1].reshape(-1,1)\n",
    "        scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "        scaler.fit(elec)\n",
    "        elec= scaler.transform(elec).reshape(1,-1)[0]\n",
    "\n",
    "        # 后项-前项\n",
    "        e=[]\n",
    "        for i in range(len(elec)):\n",
    "            if i!=23:\n",
    "                e.append(elec[i+1]-elec[i])\n",
    "            else:\n",
    "                e.append(-2)\n",
    "        e=np.array(e)\n",
    "\n",
    "        # 日期\n",
    "        p1 = r\"\\d{4}\\D\\d{1,2}\\D\\d{1,3}\\D{1}\"\n",
    "        pattern1 = re.compile(p1)\n",
    "        finish_date = pattern1.findall(files[j])\n",
    "        results=[finish_date[0]]\n",
    "\n",
    "        # 上升剧烈的前三个点\n",
    "        d1=(np.argsort(e)[-3:]+1)[::-1].tolist()\n",
    "        results.append(d1)\n",
    "\n",
    "        # 集中用电的五个点\n",
    "        d2=(np.argsort(elec)[-5:]+1)[::-1].tolist()\n",
    "        results.append(d2)\n",
    "\n",
    "        # 下降剧烈的点\n",
    "        d3=(np.argsort(e)[1:4]+1)[::-1].tolist()\n",
    "        results.append(d3)\n",
    "\n",
    "        # 计算标准差\n",
    "        std=np.std(elec)\n",
    "        results.append(std)\n",
    "\n",
    "        # 计算均值\n",
    "        mea=np.mean(data[:,1].reshape(-1,1))\n",
    "        results.append(mea)\n",
    "        result.append(results)\n",
    "names = ['日期','上升剧烈的点','集中用电的点','下降剧烈的点','标准差','均值']\n",
    "result=pd.DataFrame(data=result,columns=names)\n",
    "result.to_csv(r'E:\\中国软件杯2022\\美的暖通.csv', index=False, encoding='utf-8-sig')\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb54cc6",
   "metadata": {},
   "source": [
    "### 1.2 提取芙蓉兴国数据特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f44a7a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import re\n",
    "\n",
    "path = 'E:/中国软件杯2022/芙蓉兴国/mewtown'\n",
    "files = list(os.listdir(path))  # 读入文件夹\n",
    "for j in range(len(files)):\n",
    "    result = []\n",
    "    s='E:/中国软件杯2022/芙蓉兴国/mewtown/'+files[j]\n",
    "    file=list(os.listdir(s))\n",
    "    for k in file:\n",
    "        data=pd.read_excel(s+'/'+k)\n",
    "        data=data.loc[:23,['小时','电量（kWh）']]\n",
    "        data=np.array(data)\n",
    "        \n",
    "        # 对于整个数据倒序\n",
    "        data=data[::-1]\n",
    "        \n",
    "        # 对时间段重新编号\n",
    "        for i in range(len(data)):\n",
    "            data[i][0]=i+1\n",
    "            \n",
    "        # 标准化电量\n",
    "        elec=data[:,1].reshape(-1,1)\n",
    "        scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "        scaler.fit(elec)\n",
    "        elec= scaler.transform(elec).reshape(1,-1)[0]\n",
    "        \n",
    "        # 后项-前项\n",
    "        e=[]\n",
    "        for i in range(len(elec)):\n",
    "            if i!=23:\n",
    "                e.append(elec[i+1]-elec[i])\n",
    "            else:\n",
    "                e.append(-2)\n",
    "        e=np.array(e)\n",
    "        \n",
    "        # 日期\n",
    "        p1 = r\"\\d{4}\\D\\d{1,2}\\D\\d{1,3}\\D{1}\"\n",
    "        pattern1 = re.compile(p1)\n",
    "        finish_date = pattern1.findall(k)\n",
    "        results=[finish_date[0]]\n",
    "        \n",
    "        # 上升剧烈的前三个点\n",
    "        d1=(np.argsort(e)[-3:]+1)[::-1].tolist()\n",
    "        results.append(d1)\n",
    "        # 集中用电的五个点\n",
    "        d2=(np.argsort(elec)[-5:]+1)[::-1].tolist()\n",
    "        results.append(d2)\n",
    "        \n",
    "        # 下降剧烈的点\n",
    "        d3=(np.argsort(e)[1:4]+1)[::-1].tolist()\n",
    "        results.append(d3)\n",
    "        \n",
    "        # 计算标准差\n",
    "        std=np.std(elec)\n",
    "        results.append(std)\n",
    "        \n",
    "        # 计算均值\n",
    "        mea=np.mean(data[:,1].reshape(-1,1))\n",
    "        results.append(mea)\n",
    "        result.append(results)\n",
    "        \n",
    "    names = ['日期','上升剧烈的点','集中用电的点','下降剧烈的点','标准差','均值']\n",
    "    result=pd.DataFrame(data=result,columns=names)\n",
    "    result.to_csv(r'E:\\中国软件杯2022\\芙蓉兴国\\用户分类前\\芙蓉兴国'+files[j]+'.csv', index=False, encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a9d767",
   "metadata": {},
   "source": [
    "#### 得到结果如下所示"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "7ff3517a",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65af1f4c",
   "metadata": {},
   "source": [
    "## 2 将数据分类，分为10个类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cacc5ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "\n",
    "path = 'E:/中国软件杯2022/芙蓉兴国/用户分类前'\n",
    "files = list(os.listdir(path))  # 读入文件夹\n",
    "for f in files:\n",
    "    data=pd.read_csv(path+'/'+f)\n",
    "    std=np.array(data['标准差'])\n",
    "    mea=np.array(data['均值'])\n",
    "    # c1\n",
    "    c1=[]\n",
    "    for i in std:\n",
    "        if i<0.35:\n",
    "            c1.append(1)\n",
    "        else:\n",
    "            c1.append(2)\n",
    "    # c2\n",
    "    c2=[]\n",
    "    for i in mea:\n",
    "        if 0<=i<1:\n",
    "            c2.append(1)\n",
    "        elif 1<=i<3:\n",
    "            c2.append(2)\n",
    "        elif 3<=i<9:\n",
    "            c2.append(3)\n",
    "        elif 9<=i<20:\n",
    "            c2.append(4)\n",
    "        elif 20<=i:\n",
    "            c2.append(5)\n",
    "    # c\n",
    "    c=[]\n",
    "    for i in range(len(c1)):\n",
    "        if c1[i]==1:\n",
    "            c.append(c1[i]*c2[i])\n",
    "        else:\n",
    "            c.append(c2[i]+5)\n",
    "\n",
    "    c=pd.DataFrame(data=c,columns=['class'])\n",
    "    data=pd.concat([data,c],axis=1)\n",
    "    data.to_csv(rf'E:/中国软件杯2022/芙蓉兴国/用户分类前/{f}', index=False, encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05b4d0b",
   "metadata": {},
   "source": [
    "#### 分类结果如下所示（局部）"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "0869fe8d",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d4d536",
   "metadata": {},
   "source": [
    "## 3 从已经分类好的数据中提取出时间序列数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2df88c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>上升剧烈的点</th>\n",
       "      <th>集中用电的点</th>\n",
       "      <th>下降剧烈的点</th>\n",
       "      <th>标准差</th>\n",
       "      <th>均值</th>\n",
       "      <th>class1</th>\n",
       "      <th>class2</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017年6月14日</td>\n",
       "      <td>[8, 9, 13]</td>\n",
       "      <td>[17, 10, 11, 14, 15]</td>\n",
       "      <td>[11, 23, 12]</td>\n",
       "      <td>0.380313</td>\n",
       "      <td>10108.672460</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017年6月15日</td>\n",
       "      <td>[8, 13, 9]</td>\n",
       "      <td>[17, 19, 16, 18, 15]</td>\n",
       "      <td>[19, 12, 23]</td>\n",
       "      <td>0.365856</td>\n",
       "      <td>9997.807625</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017年6月7日</td>\n",
       "      <td>[8, 13, 9]</td>\n",
       "      <td>[15, 17, 14, 16, 11]</td>\n",
       "      <td>[17, 23, 12]</td>\n",
       "      <td>0.374307</td>\n",
       "      <td>9989.756042</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017年6月6日</td>\n",
       "      <td>[8, 13, 9]</td>\n",
       "      <td>[17, 16, 15, 14, 11]</td>\n",
       "      <td>[23, 12, 21]</td>\n",
       "      <td>0.371637</td>\n",
       "      <td>9987.942167</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017年6月16日</td>\n",
       "      <td>[8, 13, 9]</td>\n",
       "      <td>[11, 18, 17, 10, 19]</td>\n",
       "      <td>[19, 12, 23]</td>\n",
       "      <td>0.376087</td>\n",
       "      <td>9951.427083</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2017年8月8日</td>\n",
       "      <td>[17, 14, 12]</td>\n",
       "      <td>[1, 3, 2, 4, 5]</td>\n",
       "      <td>[18, 8, 5]</td>\n",
       "      <td>0.351210</td>\n",
       "      <td>1088.220458</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2017年8月9日</td>\n",
       "      <td>[8, 14, 11]</td>\n",
       "      <td>[17, 16, 18, 15, 12]</td>\n",
       "      <td>[12, 19, 18]</td>\n",
       "      <td>0.299302</td>\n",
       "      <td>887.046750</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2017年7月24日</td>\n",
       "      <td>[8, 9, 13]</td>\n",
       "      <td>[11, 17, 14, 16, 15]</td>\n",
       "      <td>[12, 21, 23]</td>\n",
       "      <td>0.404714</td>\n",
       "      <td>8366.070375</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2017年7月17日</td>\n",
       "      <td>[9, 8, 13]</td>\n",
       "      <td>[15, 16, 17, 11, 14]</td>\n",
       "      <td>[19, 23, 12]</td>\n",
       "      <td>0.403700</td>\n",
       "      <td>7923.695625</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2017年7月10日</td>\n",
       "      <td>[8, 9, 7]</td>\n",
       "      <td>[17, 18, 15, 11, 16]</td>\n",
       "      <td>[19, 12, 23]</td>\n",
       "      <td>0.404600</td>\n",
       "      <td>7645.697000</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             日期        上升剧烈的点                集中用电的点        下降剧烈的点       标准差  \\\n",
       "0    2017年6月14日    [8, 9, 13]  [17, 10, 11, 14, 15]  [11, 23, 12]  0.380313   \n",
       "1    2017年6月15日    [8, 13, 9]  [17, 19, 16, 18, 15]  [19, 12, 23]  0.365856   \n",
       "2     2017年6月7日    [8, 13, 9]  [15, 17, 14, 16, 11]  [17, 23, 12]  0.374307   \n",
       "3     2017年6月6日    [8, 13, 9]  [17, 16, 15, 14, 11]  [23, 12, 21]  0.371637   \n",
       "4    2017年6月16日    [8, 13, 9]  [11, 18, 17, 10, 19]  [19, 12, 23]  0.376087   \n",
       "..          ...           ...                   ...           ...       ...   \n",
       "110   2017年8月8日  [17, 14, 12]       [1, 3, 2, 4, 5]    [18, 8, 5]  0.351210   \n",
       "111   2017年8月9日   [8, 14, 11]  [17, 16, 18, 15, 12]  [12, 19, 18]  0.299302   \n",
       "112  2017年7月24日    [8, 9, 13]  [11, 17, 14, 16, 15]  [12, 21, 23]  0.404714   \n",
       "113  2017年7月17日    [9, 8, 13]  [15, 16, 17, 11, 14]  [19, 23, 12]  0.403700   \n",
       "114  2017年7月10日     [8, 9, 7]  [17, 18, 15, 11, 16]  [19, 12, 23]  0.404600   \n",
       "\n",
       "               均值  class1  class2  class  \n",
       "0    10108.672460       1       5      5  \n",
       "1     9997.807625       1       5      5  \n",
       "2     9989.756042       1       5      5  \n",
       "3     9987.942167       1       5      5  \n",
       "4     9951.427083       1       5      5  \n",
       "..            ...     ...     ...    ...  \n",
       "110   1088.220458       1       5      5  \n",
       "111    887.046750       1       5      5  \n",
       "112   8366.070375       2       5     10  \n",
       "113   7923.695625       2       5     10  \n",
       "114   7645.697000       2       5     10  \n",
       "\n",
       "[115 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "total_class = pd.read_csv(r'E:\\中国软件杯2022\\分类数据美的.csv',encoding='gbk')\n",
    "date_lst = total_class.iloc[:,0]\n",
    "class_lst = total_class.iloc[:,8]\n",
    "total_class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a7f116",
   "metadata": {},
   "source": [
    "### 3.1 提取所有文件夹的名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4d4aa844",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "folder = r'E:\\中国软件杯2022\\芙蓉兴国分类数据'\n",
    "furong_name_lst = [d for d in glob.glob(os.path.join(folder,'*.csv'))]\n",
    "\n",
    "folder = r'E:\\中国软件杯2022\\different'\n",
    "time_data_lst = [d for d in glob.glob(os.path.join(folder,'*.csv'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee9df249",
   "metadata": {},
   "source": [
    "### 3.2 抽取出所有时间序列数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e60de1fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>101-2015年7月10日</th>\n",
       "      <th>101-2015年7月11日</th>\n",
       "      <th>101-2015年7月12日</th>\n",
       "      <th>101-2015年7月13日</th>\n",
       "      <th>101-2015年7月14日</th>\n",
       "      <th>101-2015年7月15日</th>\n",
       "      <th>101-2015年7月16日</th>\n",
       "      <th>101-2015年7月17日</th>\n",
       "      <th>101-2015年7月18日</th>\n",
       "      <th>101-2015年7月19日</th>\n",
       "      <th>...</th>\n",
       "      <th>994-2015年7月2日</th>\n",
       "      <th>994-2015年7月30日</th>\n",
       "      <th>994-2015年7月31日</th>\n",
       "      <th>994-2015年7月3日</th>\n",
       "      <th>994-2015年7月4日</th>\n",
       "      <th>994-2015年7月5日</th>\n",
       "      <th>994-2015年7月6日</th>\n",
       "      <th>994-2015年7月7日</th>\n",
       "      <th>994-2015年7月8日</th>\n",
       "      <th>994-2015年7月9日</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.864800</td>\n",
       "      <td>4.828533</td>\n",
       "      <td>4.602600</td>\n",
       "      <td>4.004000</td>\n",
       "      <td>4.368067</td>\n",
       "      <td>3.521333</td>\n",
       "      <td>3.932733</td>\n",
       "      <td>3.909267</td>\n",
       "      <td>5.726600</td>\n",
       "      <td>5.251067</td>\n",
       "      <td>...</td>\n",
       "      <td>4.751400</td>\n",
       "      <td>8.722400</td>\n",
       "      <td>6.677000</td>\n",
       "      <td>9.084400</td>\n",
       "      <td>8.300067</td>\n",
       "      <td>1.698200</td>\n",
       "      <td>3.894200</td>\n",
       "      <td>7.265467</td>\n",
       "      <td>1.277200</td>\n",
       "      <td>8.186200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.686333</td>\n",
       "      <td>3.796667</td>\n",
       "      <td>12.802867</td>\n",
       "      <td>3.981000</td>\n",
       "      <td>4.817667</td>\n",
       "      <td>2.853067</td>\n",
       "      <td>3.554133</td>\n",
       "      <td>3.360667</td>\n",
       "      <td>4.262400</td>\n",
       "      <td>8.945533</td>\n",
       "      <td>...</td>\n",
       "      <td>8.312600</td>\n",
       "      <td>9.227333</td>\n",
       "      <td>9.168600</td>\n",
       "      <td>8.678800</td>\n",
       "      <td>7.098133</td>\n",
       "      <td>6.296400</td>\n",
       "      <td>6.069600</td>\n",
       "      <td>4.002667</td>\n",
       "      <td>10.731600</td>\n",
       "      <td>8.329200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.575133</td>\n",
       "      <td>1.550600</td>\n",
       "      <td>14.675200</td>\n",
       "      <td>2.338000</td>\n",
       "      <td>3.980267</td>\n",
       "      <td>2.230933</td>\n",
       "      <td>2.325933</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>3.166533</td>\n",
       "      <td>12.233267</td>\n",
       "      <td>...</td>\n",
       "      <td>8.419667</td>\n",
       "      <td>14.323067</td>\n",
       "      <td>9.950600</td>\n",
       "      <td>9.372000</td>\n",
       "      <td>9.590333</td>\n",
       "      <td>8.400333</td>\n",
       "      <td>8.917800</td>\n",
       "      <td>11.567267</td>\n",
       "      <td>9.216600</td>\n",
       "      <td>11.065667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.193267</td>\n",
       "      <td>4.376067</td>\n",
       "      <td>19.530067</td>\n",
       "      <td>6.356667</td>\n",
       "      <td>6.757333</td>\n",
       "      <td>5.495667</td>\n",
       "      <td>5.873133</td>\n",
       "      <td>6.910133</td>\n",
       "      <td>8.049600</td>\n",
       "      <td>6.661333</td>\n",
       "      <td>...</td>\n",
       "      <td>4.695800</td>\n",
       "      <td>11.152267</td>\n",
       "      <td>10.458800</td>\n",
       "      <td>11.144533</td>\n",
       "      <td>8.339733</td>\n",
       "      <td>9.885133</td>\n",
       "      <td>12.258867</td>\n",
       "      <td>8.701067</td>\n",
       "      <td>11.517000</td>\n",
       "      <td>7.851933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.140267</td>\n",
       "      <td>5.616733</td>\n",
       "      <td>7.989667</td>\n",
       "      <td>6.018867</td>\n",
       "      <td>7.398667</td>\n",
       "      <td>6.316333</td>\n",
       "      <td>6.732800</td>\n",
       "      <td>7.593067</td>\n",
       "      <td>8.297667</td>\n",
       "      <td>5.631000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.852467</td>\n",
       "      <td>16.307200</td>\n",
       "      <td>18.836733</td>\n",
       "      <td>8.854267</td>\n",
       "      <td>12.501867</td>\n",
       "      <td>10.222400</td>\n",
       "      <td>5.157400</td>\n",
       "      <td>10.729600</td>\n",
       "      <td>3.039467</td>\n",
       "      <td>12.920400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.255800</td>\n",
       "      <td>6.395867</td>\n",
       "      <td>8.116467</td>\n",
       "      <td>6.112133</td>\n",
       "      <td>7.882333</td>\n",
       "      <td>6.729600</td>\n",
       "      <td>8.110933</td>\n",
       "      <td>6.714200</td>\n",
       "      <td>8.277333</td>\n",
       "      <td>6.045800</td>\n",
       "      <td>...</td>\n",
       "      <td>2.985200</td>\n",
       "      <td>19.763600</td>\n",
       "      <td>12.795533</td>\n",
       "      <td>4.923467</td>\n",
       "      <td>11.079933</td>\n",
       "      <td>10.084200</td>\n",
       "      <td>10.933267</td>\n",
       "      <td>4.461200</td>\n",
       "      <td>9.435933</td>\n",
       "      <td>2.961467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.260933</td>\n",
       "      <td>7.181800</td>\n",
       "      <td>7.583467</td>\n",
       "      <td>6.451733</td>\n",
       "      <td>8.704467</td>\n",
       "      <td>6.818600</td>\n",
       "      <td>6.453533</td>\n",
       "      <td>6.435200</td>\n",
       "      <td>7.866333</td>\n",
       "      <td>6.015867</td>\n",
       "      <td>...</td>\n",
       "      <td>8.425533</td>\n",
       "      <td>7.725200</td>\n",
       "      <td>14.713933</td>\n",
       "      <td>10.540200</td>\n",
       "      <td>8.998600</td>\n",
       "      <td>12.677333</td>\n",
       "      <td>12.345667</td>\n",
       "      <td>11.434267</td>\n",
       "      <td>7.098667</td>\n",
       "      <td>12.106133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.429133</td>\n",
       "      <td>7.828867</td>\n",
       "      <td>6.415667</td>\n",
       "      <td>6.238933</td>\n",
       "      <td>8.154067</td>\n",
       "      <td>4.722800</td>\n",
       "      <td>4.575067</td>\n",
       "      <td>4.757667</td>\n",
       "      <td>8.066600</td>\n",
       "      <td>6.590267</td>\n",
       "      <td>...</td>\n",
       "      <td>1.181800</td>\n",
       "      <td>2.529133</td>\n",
       "      <td>18.663000</td>\n",
       "      <td>6.721933</td>\n",
       "      <td>3.092000</td>\n",
       "      <td>3.886800</td>\n",
       "      <td>5.233000</td>\n",
       "      <td>5.474333</td>\n",
       "      <td>5.772400</td>\n",
       "      <td>11.118267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.647000</td>\n",
       "      <td>12.617133</td>\n",
       "      <td>7.556267</td>\n",
       "      <td>7.450000</td>\n",
       "      <td>6.573467</td>\n",
       "      <td>4.552600</td>\n",
       "      <td>4.294867</td>\n",
       "      <td>5.170933</td>\n",
       "      <td>8.165400</td>\n",
       "      <td>6.132667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.221533</td>\n",
       "      <td>15.001200</td>\n",
       "      <td>11.194400</td>\n",
       "      <td>5.549400</td>\n",
       "      <td>3.388133</td>\n",
       "      <td>1.404267</td>\n",
       "      <td>3.339000</td>\n",
       "      <td>1.249667</td>\n",
       "      <td>3.579733</td>\n",
       "      <td>1.229533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.702333</td>\n",
       "      <td>7.197133</td>\n",
       "      <td>7.435400</td>\n",
       "      <td>11.086267</td>\n",
       "      <td>5.642333</td>\n",
       "      <td>4.433667</td>\n",
       "      <td>3.426667</td>\n",
       "      <td>4.450133</td>\n",
       "      <td>7.465533</td>\n",
       "      <td>5.825400</td>\n",
       "      <td>...</td>\n",
       "      <td>1.300267</td>\n",
       "      <td>9.539400</td>\n",
       "      <td>14.686733</td>\n",
       "      <td>1.392400</td>\n",
       "      <td>1.684200</td>\n",
       "      <td>1.481533</td>\n",
       "      <td>1.447800</td>\n",
       "      <td>1.343667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.188200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.435267</td>\n",
       "      <td>6.238533</td>\n",
       "      <td>7.195000</td>\n",
       "      <td>5.532533</td>\n",
       "      <td>5.615400</td>\n",
       "      <td>5.326533</td>\n",
       "      <td>3.976867</td>\n",
       "      <td>3.778133</td>\n",
       "      <td>7.870200</td>\n",
       "      <td>5.351200</td>\n",
       "      <td>...</td>\n",
       "      <td>1.201800</td>\n",
       "      <td>7.048800</td>\n",
       "      <td>10.827000</td>\n",
       "      <td>1.393600</td>\n",
       "      <td>1.821600</td>\n",
       "      <td>1.477400</td>\n",
       "      <td>1.564933</td>\n",
       "      <td>1.258667</td>\n",
       "      <td>1.528867</td>\n",
       "      <td>1.258133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.242267</td>\n",
       "      <td>5.909067</td>\n",
       "      <td>7.754800</td>\n",
       "      <td>3.635333</td>\n",
       "      <td>5.398733</td>\n",
       "      <td>5.523000</td>\n",
       "      <td>3.196467</td>\n",
       "      <td>3.588600</td>\n",
       "      <td>7.423200</td>\n",
       "      <td>5.196333</td>\n",
       "      <td>...</td>\n",
       "      <td>1.179200</td>\n",
       "      <td>6.110467</td>\n",
       "      <td>2.712800</td>\n",
       "      <td>1.548933</td>\n",
       "      <td>1.419000</td>\n",
       "      <td>1.462933</td>\n",
       "      <td>1.388467</td>\n",
       "      <td>1.400667</td>\n",
       "      <td>1.770267</td>\n",
       "      <td>1.310467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.674667</td>\n",
       "      <td>4.743800</td>\n",
       "      <td>6.641467</td>\n",
       "      <td>3.197933</td>\n",
       "      <td>3.320667</td>\n",
       "      <td>2.169333</td>\n",
       "      <td>2.533133</td>\n",
       "      <td>4.884400</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>5.845933</td>\n",
       "      <td>...</td>\n",
       "      <td>1.251667</td>\n",
       "      <td>2.322800</td>\n",
       "      <td>1.606267</td>\n",
       "      <td>1.425067</td>\n",
       "      <td>1.585267</td>\n",
       "      <td>1.603600</td>\n",
       "      <td>1.421933</td>\n",
       "      <td>1.248067</td>\n",
       "      <td>1.381067</td>\n",
       "      <td>1.211933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.634800</td>\n",
       "      <td>3.850067</td>\n",
       "      <td>7.418933</td>\n",
       "      <td>2.311867</td>\n",
       "      <td>3.630667</td>\n",
       "      <td>2.104800</td>\n",
       "      <td>2.674600</td>\n",
       "      <td>3.902800</td>\n",
       "      <td>2.761667</td>\n",
       "      <td>3.856067</td>\n",
       "      <td>...</td>\n",
       "      <td>1.291867</td>\n",
       "      <td>2.227267</td>\n",
       "      <td>1.450933</td>\n",
       "      <td>1.226733</td>\n",
       "      <td>1.543800</td>\n",
       "      <td>1.376200</td>\n",
       "      <td>1.521200</td>\n",
       "      <td>1.362267</td>\n",
       "      <td>1.453600</td>\n",
       "      <td>1.203800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.514467</td>\n",
       "      <td>3.602067</td>\n",
       "      <td>4.958667</td>\n",
       "      <td>2.799933</td>\n",
       "      <td>2.839333</td>\n",
       "      <td>2.223400</td>\n",
       "      <td>1.958267</td>\n",
       "      <td>3.392067</td>\n",
       "      <td>3.032733</td>\n",
       "      <td>5.010067</td>\n",
       "      <td>...</td>\n",
       "      <td>1.173067</td>\n",
       "      <td>1.526200</td>\n",
       "      <td>1.853933</td>\n",
       "      <td>1.210867</td>\n",
       "      <td>1.628067</td>\n",
       "      <td>1.291133</td>\n",
       "      <td>1.467800</td>\n",
       "      <td>1.247667</td>\n",
       "      <td>1.600933</td>\n",
       "      <td>1.287067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.877800</td>\n",
       "      <td>4.619733</td>\n",
       "      <td>4.558733</td>\n",
       "      <td>1.097600</td>\n",
       "      <td>3.308867</td>\n",
       "      <td>1.175000</td>\n",
       "      <td>1.732067</td>\n",
       "      <td>2.842400</td>\n",
       "      <td>2.610533</td>\n",
       "      <td>4.113467</td>\n",
       "      <td>...</td>\n",
       "      <td>1.310400</td>\n",
       "      <td>1.493800</td>\n",
       "      <td>2.675000</td>\n",
       "      <td>1.304600</td>\n",
       "      <td>1.689133</td>\n",
       "      <td>1.328267</td>\n",
       "      <td>1.453267</td>\n",
       "      <td>1.574333</td>\n",
       "      <td>2.121000</td>\n",
       "      <td>1.310533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.967267</td>\n",
       "      <td>1.635400</td>\n",
       "      <td>3.250867</td>\n",
       "      <td>2.054933</td>\n",
       "      <td>3.041400</td>\n",
       "      <td>1.932333</td>\n",
       "      <td>2.584800</td>\n",
       "      <td>4.008200</td>\n",
       "      <td>2.781400</td>\n",
       "      <td>2.494733</td>\n",
       "      <td>...</td>\n",
       "      <td>1.709867</td>\n",
       "      <td>2.202267</td>\n",
       "      <td>2.403467</td>\n",
       "      <td>2.024533</td>\n",
       "      <td>2.750133</td>\n",
       "      <td>4.047533</td>\n",
       "      <td>1.683600</td>\n",
       "      <td>1.639733</td>\n",
       "      <td>2.952733</td>\n",
       "      <td>1.746467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.607667</td>\n",
       "      <td>2.174333</td>\n",
       "      <td>3.409867</td>\n",
       "      <td>2.309000</td>\n",
       "      <td>3.471733</td>\n",
       "      <td>2.225267</td>\n",
       "      <td>2.692800</td>\n",
       "      <td>3.641000</td>\n",
       "      <td>2.875933</td>\n",
       "      <td>2.315600</td>\n",
       "      <td>...</td>\n",
       "      <td>2.555400</td>\n",
       "      <td>1.524133</td>\n",
       "      <td>1.814400</td>\n",
       "      <td>2.345533</td>\n",
       "      <td>2.211067</td>\n",
       "      <td>3.794667</td>\n",
       "      <td>2.585467</td>\n",
       "      <td>4.974067</td>\n",
       "      <td>2.085667</td>\n",
       "      <td>2.276133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.630067</td>\n",
       "      <td>2.180733</td>\n",
       "      <td>3.854600</td>\n",
       "      <td>2.923600</td>\n",
       "      <td>3.548000</td>\n",
       "      <td>2.272933</td>\n",
       "      <td>2.847267</td>\n",
       "      <td>3.989867</td>\n",
       "      <td>2.731667</td>\n",
       "      <td>2.483667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.565733</td>\n",
       "      <td>4.160467</td>\n",
       "      <td>1.361867</td>\n",
       "      <td>1.265667</td>\n",
       "      <td>1.443533</td>\n",
       "      <td>2.718933</td>\n",
       "      <td>2.031267</td>\n",
       "      <td>2.185200</td>\n",
       "      <td>1.280333</td>\n",
       "      <td>1.535067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.825400</td>\n",
       "      <td>1.669667</td>\n",
       "      <td>4.330533</td>\n",
       "      <td>2.489667</td>\n",
       "      <td>3.828333</td>\n",
       "      <td>2.999667</td>\n",
       "      <td>3.536867</td>\n",
       "      <td>4.712600</td>\n",
       "      <td>2.347467</td>\n",
       "      <td>3.580667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.352800</td>\n",
       "      <td>1.605600</td>\n",
       "      <td>1.257600</td>\n",
       "      <td>1.092667</td>\n",
       "      <td>1.710933</td>\n",
       "      <td>2.121333</td>\n",
       "      <td>1.287133</td>\n",
       "      <td>1.295067</td>\n",
       "      <td>1.238667</td>\n",
       "      <td>1.227733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.575600</td>\n",
       "      <td>1.931467</td>\n",
       "      <td>4.226600</td>\n",
       "      <td>2.340400</td>\n",
       "      <td>3.382000</td>\n",
       "      <td>3.123600</td>\n",
       "      <td>2.773933</td>\n",
       "      <td>4.048000</td>\n",
       "      <td>2.488400</td>\n",
       "      <td>3.630200</td>\n",
       "      <td>...</td>\n",
       "      <td>1.143667</td>\n",
       "      <td>2.619667</td>\n",
       "      <td>6.899733</td>\n",
       "      <td>4.602133</td>\n",
       "      <td>6.364333</td>\n",
       "      <td>5.187467</td>\n",
       "      <td>1.264933</td>\n",
       "      <td>1.285400</td>\n",
       "      <td>5.610400</td>\n",
       "      <td>1.917067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.419800</td>\n",
       "      <td>2.082333</td>\n",
       "      <td>4.058800</td>\n",
       "      <td>3.439467</td>\n",
       "      <td>3.523133</td>\n",
       "      <td>3.508467</td>\n",
       "      <td>3.054067</td>\n",
       "      <td>4.068667</td>\n",
       "      <td>3.062667</td>\n",
       "      <td>4.451933</td>\n",
       "      <td>...</td>\n",
       "      <td>1.577667</td>\n",
       "      <td>8.426133</td>\n",
       "      <td>6.118933</td>\n",
       "      <td>4.150333</td>\n",
       "      <td>4.558000</td>\n",
       "      <td>4.780800</td>\n",
       "      <td>7.214867</td>\n",
       "      <td>7.103667</td>\n",
       "      <td>8.009667</td>\n",
       "      <td>7.111133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.136400</td>\n",
       "      <td>2.587400</td>\n",
       "      <td>4.184667</td>\n",
       "      <td>4.273400</td>\n",
       "      <td>3.832867</td>\n",
       "      <td>3.841200</td>\n",
       "      <td>2.854667</td>\n",
       "      <td>4.207133</td>\n",
       "      <td>3.369133</td>\n",
       "      <td>5.022400</td>\n",
       "      <td>...</td>\n",
       "      <td>6.985067</td>\n",
       "      <td>8.558467</td>\n",
       "      <td>8.621400</td>\n",
       "      <td>6.307067</td>\n",
       "      <td>6.259333</td>\n",
       "      <td>4.674933</td>\n",
       "      <td>6.272467</td>\n",
       "      <td>6.928800</td>\n",
       "      <td>7.325800</td>\n",
       "      <td>5.923533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.641533</td>\n",
       "      <td>2.399933</td>\n",
       "      <td>5.000867</td>\n",
       "      <td>4.428200</td>\n",
       "      <td>4.248467</td>\n",
       "      <td>4.240133</td>\n",
       "      <td>3.163200</td>\n",
       "      <td>3.879933</td>\n",
       "      <td>3.387800</td>\n",
       "      <td>4.843600</td>\n",
       "      <td>...</td>\n",
       "      <td>2.622133</td>\n",
       "      <td>8.683000</td>\n",
       "      <td>8.714667</td>\n",
       "      <td>1.134000</td>\n",
       "      <td>3.994133</td>\n",
       "      <td>1.622200</td>\n",
       "      <td>8.006133</td>\n",
       "      <td>4.496867</td>\n",
       "      <td>2.907200</td>\n",
       "      <td>6.194200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 6510 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    101-2015年7月10日  101-2015年7月11日  101-2015年7月12日  101-2015年7月13日  \\\n",
       "0         2.864800        4.828533        4.602600        4.004000   \n",
       "1         3.686333        3.796667       12.802867        3.981000   \n",
       "2         2.575133        1.550600       14.675200        2.338000   \n",
       "3         6.193267        4.376067       19.530067        6.356667   \n",
       "4         7.140267        5.616733        7.989667        6.018867   \n",
       "5         8.255800        6.395867        8.116467        6.112133   \n",
       "6        10.260933        7.181800        7.583467        6.451733   \n",
       "7        20.429133        7.828867        6.415667        6.238933   \n",
       "8         7.647000       12.617133        7.556267        7.450000   \n",
       "9         4.702333        7.197133        7.435400       11.086267   \n",
       "10        4.435267        6.238533        7.195000        5.532533   \n",
       "11        5.242267        5.909067        7.754800        3.635333   \n",
       "12        5.674667        4.743800        6.641467        3.197933   \n",
       "13        4.634800        3.850067        7.418933        2.311867   \n",
       "14        3.514467        3.602067        4.958667        2.799933   \n",
       "15        2.877800        4.619733        4.558733        1.097600   \n",
       "16        2.967267        1.635400        3.250867        2.054933   \n",
       "17        3.607667        2.174333        3.409867        2.309000   \n",
       "18        2.630067        2.180733        3.854600        2.923600   \n",
       "19        3.825400        1.669667        4.330533        2.489667   \n",
       "20        3.575600        1.931467        4.226600        2.340400   \n",
       "21        2.419800        2.082333        4.058800        3.439467   \n",
       "22        3.136400        2.587400        4.184667        4.273400   \n",
       "23        3.641533        2.399933        5.000867        4.428200   \n",
       "24        3.000000        3.000000        3.000000        3.000000   \n",
       "\n",
       "    101-2015年7月14日  101-2015年7月15日  101-2015年7月16日  101-2015年7月17日  \\\n",
       "0         4.368067        3.521333        3.932733        3.909267   \n",
       "1         4.817667        2.853067        3.554133        3.360667   \n",
       "2         3.980267        2.230933        2.325933        2.540000   \n",
       "3         6.757333        5.495667        5.873133        6.910133   \n",
       "4         7.398667        6.316333        6.732800        7.593067   \n",
       "5         7.882333        6.729600        8.110933        6.714200   \n",
       "6         8.704467        6.818600        6.453533        6.435200   \n",
       "7         8.154067        4.722800        4.575067        4.757667   \n",
       "8         6.573467        4.552600        4.294867        5.170933   \n",
       "9         5.642333        4.433667        3.426667        4.450133   \n",
       "10        5.615400        5.326533        3.976867        3.778133   \n",
       "11        5.398733        5.523000        3.196467        3.588600   \n",
       "12        3.320667        2.169333        2.533133        4.884400   \n",
       "13        3.630667        2.104800        2.674600        3.902800   \n",
       "14        2.839333        2.223400        1.958267        3.392067   \n",
       "15        3.308867        1.175000        1.732067        2.842400   \n",
       "16        3.041400        1.932333        2.584800        4.008200   \n",
       "17        3.471733        2.225267        2.692800        3.641000   \n",
       "18        3.548000        2.272933        2.847267        3.989867   \n",
       "19        3.828333        2.999667        3.536867        4.712600   \n",
       "20        3.382000        3.123600        2.773933        4.048000   \n",
       "21        3.523133        3.508467        3.054067        4.068667   \n",
       "22        3.832867        3.841200        2.854667        4.207133   \n",
       "23        4.248467        4.240133        3.163200        3.879933   \n",
       "24        3.000000        3.000000        3.000000        3.000000   \n",
       "\n",
       "    101-2015年7月18日  101-2015年7月19日  ...  994-2015年7月2日  994-2015年7月30日  \\\n",
       "0         5.726600        5.251067  ...       4.751400        8.722400   \n",
       "1         4.262400        8.945533  ...       8.312600        9.227333   \n",
       "2         3.166533       12.233267  ...       8.419667       14.323067   \n",
       "3         8.049600        6.661333  ...       4.695800       11.152267   \n",
       "4         8.297667        5.631000  ...      12.852467       16.307200   \n",
       "5         8.277333        6.045800  ...       2.985200       19.763600   \n",
       "6         7.866333        6.015867  ...       8.425533        7.725200   \n",
       "7         8.066600        6.590267  ...       1.181800        2.529133   \n",
       "8         8.165400        6.132667  ...       1.221533       15.001200   \n",
       "9         7.465533        5.825400  ...       1.300267        9.539400   \n",
       "10        7.870200        5.351200  ...       1.201800        7.048800   \n",
       "11        7.423200        5.196333  ...       1.179200        6.110467   \n",
       "12        4.710000        5.845933  ...       1.251667        2.322800   \n",
       "13        2.761667        3.856067  ...       1.291867        2.227267   \n",
       "14        3.032733        5.010067  ...       1.173067        1.526200   \n",
       "15        2.610533        4.113467  ...       1.310400        1.493800   \n",
       "16        2.781400        2.494733  ...       1.709867        2.202267   \n",
       "17        2.875933        2.315600  ...       2.555400        1.524133   \n",
       "18        2.731667        2.483667  ...       1.565733        4.160467   \n",
       "19        2.347467        3.580667  ...       1.352800        1.605600   \n",
       "20        2.488400        3.630200  ...       1.143667        2.619667   \n",
       "21        3.062667        4.451933  ...       1.577667        8.426133   \n",
       "22        3.369133        5.022400  ...       6.985067        8.558467   \n",
       "23        3.387800        4.843600  ...       2.622133        8.683000   \n",
       "24        8.000000        3.000000  ...       3.000000        3.000000   \n",
       "\n",
       "    994-2015年7月31日  994-2015年7月3日  994-2015年7月4日  994-2015年7月5日  \\\n",
       "0         6.677000       9.084400       8.300067       1.698200   \n",
       "1         9.168600       8.678800       7.098133       6.296400   \n",
       "2         9.950600       9.372000       9.590333       8.400333   \n",
       "3        10.458800      11.144533       8.339733       9.885133   \n",
       "4        18.836733       8.854267      12.501867      10.222400   \n",
       "5        12.795533       4.923467      11.079933      10.084200   \n",
       "6        14.713933      10.540200       8.998600      12.677333   \n",
       "7        18.663000       6.721933       3.092000       3.886800   \n",
       "8        11.194400       5.549400       3.388133       1.404267   \n",
       "9        14.686733       1.392400       1.684200       1.481533   \n",
       "10       10.827000       1.393600       1.821600       1.477400   \n",
       "11        2.712800       1.548933       1.419000       1.462933   \n",
       "12        1.606267       1.425067       1.585267       1.603600   \n",
       "13        1.450933       1.226733       1.543800       1.376200   \n",
       "14        1.853933       1.210867       1.628067       1.291133   \n",
       "15        2.675000       1.304600       1.689133       1.328267   \n",
       "16        2.403467       2.024533       2.750133       4.047533   \n",
       "17        1.814400       2.345533       2.211067       3.794667   \n",
       "18        1.361867       1.265667       1.443533       2.718933   \n",
       "19        1.257600       1.092667       1.710933       2.121333   \n",
       "20        6.899733       4.602133       6.364333       5.187467   \n",
       "21        6.118933       4.150333       4.558000       4.780800   \n",
       "22        8.621400       6.307067       6.259333       4.674933   \n",
       "23        8.714667       1.134000       3.994133       1.622200   \n",
       "24        3.000000       3.000000       3.000000       3.000000   \n",
       "\n",
       "    994-2015年7月6日  994-2015年7月7日  994-2015年7月8日  994-2015年7月9日  \n",
       "0        3.894200       7.265467       1.277200       8.186200  \n",
       "1        6.069600       4.002667      10.731600       8.329200  \n",
       "2        8.917800      11.567267       9.216600      11.065667  \n",
       "3       12.258867       8.701067      11.517000       7.851933  \n",
       "4        5.157400      10.729600       3.039467      12.920400  \n",
       "5       10.933267       4.461200       9.435933       2.961467  \n",
       "6       12.345667      11.434267       7.098667      12.106133  \n",
       "7        5.233000       5.474333       5.772400      11.118267  \n",
       "8        3.339000       1.249667       3.579733       1.229533  \n",
       "9        1.447800       1.343667       1.603733       1.188200  \n",
       "10       1.564933       1.258667       1.528867       1.258133  \n",
       "11       1.388467       1.400667       1.770267       1.310467  \n",
       "12       1.421933       1.248067       1.381067       1.211933  \n",
       "13       1.521200       1.362267       1.453600       1.203800  \n",
       "14       1.467800       1.247667       1.600933       1.287067  \n",
       "15       1.453267       1.574333       2.121000       1.310533  \n",
       "16       1.683600       1.639733       2.952733       1.746467  \n",
       "17       2.585467       4.974067       2.085667       2.276133  \n",
       "18       2.031267       2.185200       1.280333       1.535067  \n",
       "19       1.287133       1.295067       1.238667       1.227733  \n",
       "20       1.264933       1.285400       5.610400       1.917067  \n",
       "21       7.214867       7.103667       8.009667       7.111133  \n",
       "22       6.272467       6.928800       7.325800       5.923533  \n",
       "23       8.006133       4.496867       2.907200       6.194200  \n",
       "24       3.000000       3.000000       3.000000       3.000000  \n",
       "\n",
       "[25 rows x 6510 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total_simple = pd.DataFrame()\n",
    "for i in furong_name_lst:\n",
    "    temp_user_id = re.search(\"(\\d{2,4}).csv\",f\"{i}\").group(1)\n",
    "    temp_data = pd.read_csv(rf'{i}')\n",
    "    temp_date_same_user = temp_data['日期']\n",
    "    for j in range(len(temp_date_same_user)):\n",
    "        temp_trans_date = temp_date_same_user[j].replace('月','-')\n",
    "        temp_trans_date = temp_trans_date.replace('年','-')\n",
    "        temp_trans_date = temp_trans_date.replace('日','')\n",
    "        temp_class = temp_data.iloc[j,6]\n",
    "        for k in time_data_lst:\n",
    "            try:\n",
    "                temp_user_date_data_name = re.search(fr'.*(用户{temp_user_id}及{temp_trans_date}.csv)',k).group(1)\n",
    "            except:\n",
    "                continue\n",
    "        temp_user_date_data = pd.read_csv(rf'E:\\中国软件杯2022\\different\\{temp_user_date_data_name}')\n",
    "        temp_power_data = temp_user_date_data.iloc[:,2].tolist()\n",
    "        temp_power_data.append(temp_class)\n",
    "        temp_one_row = pd.DataFrame(temp_power_data,columns=[f'{temp_user_id}-{temp_date_same_user[j]}'])\n",
    "        df_total_simple = pd.concat([df_total_simple,temp_one_row],axis=1)\n",
    "df_total_simple"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0734736f",
   "metadata": {},
   "source": [
    "### 3.3 将数据存入result.csv文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3f6418b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total_simple.to_csv(r'E:\\中国软件杯2022\\result.csv',index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc6b629c",
   "metadata": {},
   "source": [
    "### 3.4 另一个数据使用同样方法提取时间序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "61f8c25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "folder = r'E:\\中国软件杯2022\\美的暖通\\美的暖通'\n",
    "time_data_lst = [d for d in glob.glob(os.path.join(folder,'*.xls'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e71a3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>美的暖通-2017年6月14日</th>\n",
       "      <th>美的暖通-2017年6月15日</th>\n",
       "      <th>美的暖通-2017年6月7日</th>\n",
       "      <th>美的暖通-2017年6月6日</th>\n",
       "      <th>美的暖通-2017年6月16日</th>\n",
       "      <th>美的暖通-2017年6月8日</th>\n",
       "      <th>美的暖通-2017年6月27日</th>\n",
       "      <th>美的暖通-2017年6月22日</th>\n",
       "      <th>美的暖通-2017年6月13日</th>\n",
       "      <th>美的暖通-2017年6月9日</th>\n",
       "      <th>...</th>\n",
       "      <th>美的暖通-2017年4月29日</th>\n",
       "      <th>美的暖通-2017年7月9日</th>\n",
       "      <th>美的暖通-2017年8月11日</th>\n",
       "      <th>美的暖通-2017年5月30日</th>\n",
       "      <th>美的暖通-2017年8月10日</th>\n",
       "      <th>美的暖通-2017年8月8日</th>\n",
       "      <th>美的暖通-2017年8月9日</th>\n",
       "      <th>美的暖通-2017年7月24日</th>\n",
       "      <th>美的暖通-2017年7月17日</th>\n",
       "      <th>美的暖通-2017年7月10日</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6976.522</td>\n",
       "      <td>6664.072</td>\n",
       "      <td>6847.061</td>\n",
       "      <td>6535.154</td>\n",
       "      <td>7192.544</td>\n",
       "      <td>6914.148</td>\n",
       "      <td>6521.641</td>\n",
       "      <td>6441.265</td>\n",
       "      <td>6454.352</td>\n",
       "      <td>6942.467</td>\n",
       "      <td>...</td>\n",
       "      <td>4429.792</td>\n",
       "      <td>5183.657</td>\n",
       "      <td>1413.178</td>\n",
       "      <td>3795.009</td>\n",
       "      <td>827.472</td>\n",
       "      <td>2284.320</td>\n",
       "      <td>706.861</td>\n",
       "      <td>3087.511</td>\n",
       "      <td>3310.951</td>\n",
       "      <td>2018.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7382.356</td>\n",
       "      <td>6729.576</td>\n",
       "      <td>6959.704</td>\n",
       "      <td>6452.257</td>\n",
       "      <td>7490.105</td>\n",
       "      <td>7171.761</td>\n",
       "      <td>6753.900</td>\n",
       "      <td>6822.256</td>\n",
       "      <td>6777.852</td>\n",
       "      <td>6997.808</td>\n",
       "      <td>...</td>\n",
       "      <td>4179.673</td>\n",
       "      <td>5218.813</td>\n",
       "      <td>1462.903</td>\n",
       "      <td>3768.801</td>\n",
       "      <td>793.855</td>\n",
       "      <td>2213.339</td>\n",
       "      <td>669.093</td>\n",
       "      <td>3308.087</td>\n",
       "      <td>3423.029</td>\n",
       "      <td>2117.549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7304.137</td>\n",
       "      <td>6776.232</td>\n",
       "      <td>6962.575</td>\n",
       "      <td>6461.012</td>\n",
       "      <td>7272.688</td>\n",
       "      <td>6957.120</td>\n",
       "      <td>6575.627</td>\n",
       "      <td>6898.243</td>\n",
       "      <td>6839.785</td>\n",
       "      <td>6941.456</td>\n",
       "      <td>...</td>\n",
       "      <td>4201.852</td>\n",
       "      <td>5162.541</td>\n",
       "      <td>1487.303</td>\n",
       "      <td>3703.269</td>\n",
       "      <td>791.380</td>\n",
       "      <td>2227.660</td>\n",
       "      <td>621.136</td>\n",
       "      <td>3248.676</td>\n",
       "      <td>3377.778</td>\n",
       "      <td>2164.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7208.559</td>\n",
       "      <td>6627.644</td>\n",
       "      <td>6743.434</td>\n",
       "      <td>6274.364</td>\n",
       "      <td>6948.480</td>\n",
       "      <td>6614.329</td>\n",
       "      <td>6255.873</td>\n",
       "      <td>6568.138</td>\n",
       "      <td>6625.684</td>\n",
       "      <td>6761.413</td>\n",
       "      <td>...</td>\n",
       "      <td>4004.179</td>\n",
       "      <td>5082.995</td>\n",
       "      <td>1364.288</td>\n",
       "      <td>3528.967</td>\n",
       "      <td>767.345</td>\n",
       "      <td>2195.089</td>\n",
       "      <td>613.400</td>\n",
       "      <td>3103.760</td>\n",
       "      <td>3345.465</td>\n",
       "      <td>2147.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6976.657</td>\n",
       "      <td>6473.433</td>\n",
       "      <td>6625.765</td>\n",
       "      <td>6219.593</td>\n",
       "      <td>6952.443</td>\n",
       "      <td>6655.436</td>\n",
       "      <td>6281.834</td>\n",
       "      <td>6639.016</td>\n",
       "      <td>6664.170</td>\n",
       "      <td>6760.006</td>\n",
       "      <td>...</td>\n",
       "      <td>3927.068</td>\n",
       "      <td>4704.864</td>\n",
       "      <td>1439.261</td>\n",
       "      <td>3436.389</td>\n",
       "      <td>789.136</td>\n",
       "      <td>1978.386</td>\n",
       "      <td>623.375</td>\n",
       "      <td>3059.920</td>\n",
       "      <td>3304.632</td>\n",
       "      <td>2094.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7165.116</td>\n",
       "      <td>6685.203</td>\n",
       "      <td>6748.543</td>\n",
       "      <td>6384.397</td>\n",
       "      <td>7109.747</td>\n",
       "      <td>6736.563</td>\n",
       "      <td>6322.172</td>\n",
       "      <td>6852.707</td>\n",
       "      <td>6724.590</td>\n",
       "      <td>7069.623</td>\n",
       "      <td>...</td>\n",
       "      <td>4054.001</td>\n",
       "      <td>4881.182</td>\n",
       "      <td>1471.840</td>\n",
       "      <td>3598.205</td>\n",
       "      <td>772.941</td>\n",
       "      <td>1112.934</td>\n",
       "      <td>607.977</td>\n",
       "      <td>3281.080</td>\n",
       "      <td>3477.068</td>\n",
       "      <td>2170.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7172.936</td>\n",
       "      <td>6750.953</td>\n",
       "      <td>6597.017</td>\n",
       "      <td>6526.251</td>\n",
       "      <td>7061.544</td>\n",
       "      <td>6765.006</td>\n",
       "      <td>6368.213</td>\n",
       "      <td>7021.163</td>\n",
       "      <td>6689.710</td>\n",
       "      <td>6906.079</td>\n",
       "      <td>...</td>\n",
       "      <td>4027.502</td>\n",
       "      <td>5261.654</td>\n",
       "      <td>1460.223</td>\n",
       "      <td>3477.131</td>\n",
       "      <td>816.409</td>\n",
       "      <td>951.112</td>\n",
       "      <td>613.499</td>\n",
       "      <td>3249.281</td>\n",
       "      <td>3457.824</td>\n",
       "      <td>2236.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8255.404</td>\n",
       "      <td>7761.988</td>\n",
       "      <td>7729.831</td>\n",
       "      <td>7747.975</td>\n",
       "      <td>8295.015</td>\n",
       "      <td>8148.652</td>\n",
       "      <td>7659.910</td>\n",
       "      <td>8111.137</td>\n",
       "      <td>7836.732</td>\n",
       "      <td>8110.130</td>\n",
       "      <td>...</td>\n",
       "      <td>4813.448</td>\n",
       "      <td>4664.768</td>\n",
       "      <td>1695.763</td>\n",
       "      <td>2831.472</td>\n",
       "      <td>1151.189</td>\n",
       "      <td>1030.148</td>\n",
       "      <td>699.139</td>\n",
       "      <td>4780.648</td>\n",
       "      <td>4941.299</td>\n",
       "      <td>4379.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10654.448</td>\n",
       "      <td>10306.798</td>\n",
       "      <td>10160.571</td>\n",
       "      <td>10579.151</td>\n",
       "      <td>10829.080</td>\n",
       "      <td>10404.080</td>\n",
       "      <td>10044.926</td>\n",
       "      <td>10563.200</td>\n",
       "      <td>10123.943</td>\n",
       "      <td>10369.407</td>\n",
       "      <td>...</td>\n",
       "      <td>5739.037</td>\n",
       "      <td>5742.621</td>\n",
       "      <td>3957.502</td>\n",
       "      <td>2474.321</td>\n",
       "      <td>2099.421</td>\n",
       "      <td>665.120</td>\n",
       "      <td>1050.896</td>\n",
       "      <td>8401.331</td>\n",
       "      <td>7196.496</td>\n",
       "      <td>8210.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12505.901</td>\n",
       "      <td>11582.937</td>\n",
       "      <td>11950.297</td>\n",
       "      <td>12354.701</td>\n",
       "      <td>12254.890</td>\n",
       "      <td>11975.208</td>\n",
       "      <td>12315.033</td>\n",
       "      <td>12130.294</td>\n",
       "      <td>11497.093</td>\n",
       "      <td>11897.409</td>\n",
       "      <td>...</td>\n",
       "      <td>6075.732</td>\n",
       "      <td>6611.725</td>\n",
       "      <td>6196.336</td>\n",
       "      <td>2347.460</td>\n",
       "      <td>2433.026</td>\n",
       "      <td>685.623</td>\n",
       "      <td>1025.528</td>\n",
       "      <td>11605.523</td>\n",
       "      <td>10375.924</td>\n",
       "      <td>10666.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12325.646</td>\n",
       "      <td>12117.087</td>\n",
       "      <td>12491.300</td>\n",
       "      <td>12769.085</td>\n",
       "      <td>12715.611</td>\n",
       "      <td>12554.487</td>\n",
       "      <td>12929.438</td>\n",
       "      <td>12312.313</td>\n",
       "      <td>11952.754</td>\n",
       "      <td>12055.994</td>\n",
       "      <td>...</td>\n",
       "      <td>6066.967</td>\n",
       "      <td>7109.823</td>\n",
       "      <td>7335.243</td>\n",
       "      <td>2352.470</td>\n",
       "      <td>2389.656</td>\n",
       "      <td>701.716</td>\n",
       "      <td>966.545</td>\n",
       "      <td>12161.561</td>\n",
       "      <td>11403.978</td>\n",
       "      <td>11172.628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11539.733</td>\n",
       "      <td>11399.647</td>\n",
       "      <td>11952.849</td>\n",
       "      <td>11908.215</td>\n",
       "      <td>11742.651</td>\n",
       "      <td>11882.280</td>\n",
       "      <td>12254.733</td>\n",
       "      <td>10728.630</td>\n",
       "      <td>11001.187</td>\n",
       "      <td>11273.673</td>\n",
       "      <td>...</td>\n",
       "      <td>5376.436</td>\n",
       "      <td>6841.625</td>\n",
       "      <td>7556.609</td>\n",
       "      <td>2318.565</td>\n",
       "      <td>2047.149</td>\n",
       "      <td>709.346</td>\n",
       "      <td>1082.216</td>\n",
       "      <td>11489.484</td>\n",
       "      <td>10938.689</td>\n",
       "      <td>10767.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10451.603</td>\n",
       "      <td>10294.923</td>\n",
       "      <td>10511.052</td>\n",
       "      <td>10712.121</td>\n",
       "      <td>10344.623</td>\n",
       "      <td>10388.407</td>\n",
       "      <td>11159.564</td>\n",
       "      <td>9902.748</td>\n",
       "      <td>9524.399</td>\n",
       "      <td>9875.801</td>\n",
       "      <td>...</td>\n",
       "      <td>4180.133</td>\n",
       "      <td>6805.765</td>\n",
       "      <td>7035.952</td>\n",
       "      <td>2266.111</td>\n",
       "      <td>1816.291</td>\n",
       "      <td>794.730</td>\n",
       "      <td>954.904</td>\n",
       "      <td>10112.750</td>\n",
       "      <td>9467.324</td>\n",
       "      <td>9414.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12270.559</td>\n",
       "      <td>12293.241</td>\n",
       "      <td>12706.397</td>\n",
       "      <td>12920.156</td>\n",
       "      <td>12131.025</td>\n",
       "      <td>12472.169</td>\n",
       "      <td>12843.822</td>\n",
       "      <td>11832.575</td>\n",
       "      <td>11594.373</td>\n",
       "      <td>12189.511</td>\n",
       "      <td>...</td>\n",
       "      <td>6147.829</td>\n",
       "      <td>6499.933</td>\n",
       "      <td>7578.960</td>\n",
       "      <td>2333.731</td>\n",
       "      <td>2119.952</td>\n",
       "      <td>773.734</td>\n",
       "      <td>980.348</td>\n",
       "      <td>11811.316</td>\n",
       "      <td>11181.652</td>\n",
       "      <td>10965.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12208.224</td>\n",
       "      <td>12471.940</td>\n",
       "      <td>13064.172</td>\n",
       "      <td>12976.776</td>\n",
       "      <td>12117.560</td>\n",
       "      <td>12459.924</td>\n",
       "      <td>13042.686</td>\n",
       "      <td>12002.991</td>\n",
       "      <td>11843.340</td>\n",
       "      <td>12291.267</td>\n",
       "      <td>...</td>\n",
       "      <td>6478.068</td>\n",
       "      <td>6042.089</td>\n",
       "      <td>7965.701</td>\n",
       "      <td>2372.833</td>\n",
       "      <td>2261.507</td>\n",
       "      <td>1006.973</td>\n",
       "      <td>1140.508</td>\n",
       "      <td>11629.996</td>\n",
       "      <td>11458.112</td>\n",
       "      <td>11205.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12142.892</td>\n",
       "      <td>12634.294</td>\n",
       "      <td>12698.815</td>\n",
       "      <td>13110.601</td>\n",
       "      <td>12037.679</td>\n",
       "      <td>12154.123</td>\n",
       "      <td>12626.411</td>\n",
       "      <td>11694.314</td>\n",
       "      <td>12171.221</td>\n",
       "      <td>12012.051</td>\n",
       "      <td>...</td>\n",
       "      <td>5660.681</td>\n",
       "      <td>4906.275</td>\n",
       "      <td>7438.041</td>\n",
       "      <td>2352.779</td>\n",
       "      <td>2188.487</td>\n",
       "      <td>869.103</td>\n",
       "      <td>1244.219</td>\n",
       "      <td>11668.462</td>\n",
       "      <td>11407.769</td>\n",
       "      <td>11096.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12577.511</td>\n",
       "      <td>13141.527</td>\n",
       "      <td>13059.479</td>\n",
       "      <td>13335.454</td>\n",
       "      <td>12629.166</td>\n",
       "      <td>12312.401</td>\n",
       "      <td>12235.634</td>\n",
       "      <td>11921.555</td>\n",
       "      <td>12469.041</td>\n",
       "      <td>11973.495</td>\n",
       "      <td>...</td>\n",
       "      <td>5365.868</td>\n",
       "      <td>2729.154</td>\n",
       "      <td>6584.592</td>\n",
       "      <td>2366.054</td>\n",
       "      <td>2151.994</td>\n",
       "      <td>762.896</td>\n",
       "      <td>1324.763</td>\n",
       "      <td>12007.447</td>\n",
       "      <td>11404.577</td>\n",
       "      <td>11486.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12044.568</td>\n",
       "      <td>12590.881</td>\n",
       "      <td>12390.073</td>\n",
       "      <td>12327.019</td>\n",
       "      <td>12713.449</td>\n",
       "      <td>11927.375</td>\n",
       "      <td>11693.960</td>\n",
       "      <td>12013.351</td>\n",
       "      <td>12243.295</td>\n",
       "      <td>11375.185</td>\n",
       "      <td>...</td>\n",
       "      <td>4432.568</td>\n",
       "      <td>2581.064</td>\n",
       "      <td>6139.991</td>\n",
       "      <td>2264.116</td>\n",
       "      <td>2175.409</td>\n",
       "      <td>1033.207</td>\n",
       "      <td>1198.379</td>\n",
       "      <td>11574.163</td>\n",
       "      <td>11152.120</td>\n",
       "      <td>11342.713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12119.850</td>\n",
       "      <td>12672.084</td>\n",
       "      <td>12134.449</td>\n",
       "      <td>12020.669</td>\n",
       "      <td>12178.015</td>\n",
       "      <td>11943.591</td>\n",
       "      <td>11289.855</td>\n",
       "      <td>12035.031</td>\n",
       "      <td>12188.242</td>\n",
       "      <td>11137.543</td>\n",
       "      <td>...</td>\n",
       "      <td>4215.878</td>\n",
       "      <td>2300.840</td>\n",
       "      <td>5696.883</td>\n",
       "      <td>2016.541</td>\n",
       "      <td>1899.929</td>\n",
       "      <td>794.576</td>\n",
       "      <td>1021.604</td>\n",
       "      <td>11625.599</td>\n",
       "      <td>10772.551</td>\n",
       "      <td>10978.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11401.189</td>\n",
       "      <td>11933.072</td>\n",
       "      <td>11589.519</td>\n",
       "      <td>11762.121</td>\n",
       "      <td>10867.836</td>\n",
       "      <td>11159.813</td>\n",
       "      <td>10804.440</td>\n",
       "      <td>11329.140</td>\n",
       "      <td>11464.464</td>\n",
       "      <td>10649.232</td>\n",
       "      <td>...</td>\n",
       "      <td>4403.736</td>\n",
       "      <td>2115.748</td>\n",
       "      <td>5487.915</td>\n",
       "      <td>2181.770</td>\n",
       "      <td>1550.609</td>\n",
       "      <td>712.864</td>\n",
       "      <td>855.756</td>\n",
       "      <td>11262.184</td>\n",
       "      <td>9913.405</td>\n",
       "      <td>10235.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11445.381</td>\n",
       "      <td>11681.599</td>\n",
       "      <td>10922.517</td>\n",
       "      <td>11534.700</td>\n",
       "      <td>10362.015</td>\n",
       "      <td>10648.387</td>\n",
       "      <td>10953.620</td>\n",
       "      <td>11387.052</td>\n",
       "      <td>11138.928</td>\n",
       "      <td>10684.899</td>\n",
       "      <td>...</td>\n",
       "      <td>4285.840</td>\n",
       "      <td>2200.870</td>\n",
       "      <td>5337.982</td>\n",
       "      <td>2328.958</td>\n",
       "      <td>1535.863</td>\n",
       "      <td>681.093</td>\n",
       "      <td>851.064</td>\n",
       "      <td>11203.038</td>\n",
       "      <td>9975.845</td>\n",
       "      <td>10128.441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10779.817</td>\n",
       "      <td>10997.511</td>\n",
       "      <td>10278.434</td>\n",
       "      <td>10177.559</td>\n",
       "      <td>9750.716</td>\n",
       "      <td>9828.431</td>\n",
       "      <td>10198.716</td>\n",
       "      <td>10582.761</td>\n",
       "      <td>10775.795</td>\n",
       "      <td>9927.462</td>\n",
       "      <td>...</td>\n",
       "      <td>4157.538</td>\n",
       "      <td>2195.671</td>\n",
       "      <td>4755.183</td>\n",
       "      <td>2353.460</td>\n",
       "      <td>1524.923</td>\n",
       "      <td>649.588</td>\n",
       "      <td>816.625</td>\n",
       "      <td>9809.002</td>\n",
       "      <td>9251.399</td>\n",
       "      <td>9560.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10386.821</td>\n",
       "      <td>10497.034</td>\n",
       "      <td>9947.927</td>\n",
       "      <td>9821.216</td>\n",
       "      <td>9647.695</td>\n",
       "      <td>9645.835</td>\n",
       "      <td>9827.152</td>\n",
       "      <td>10029.276</td>\n",
       "      <td>10401.284</td>\n",
       "      <td>9482.101</td>\n",
       "      <td>...</td>\n",
       "      <td>3168.320</td>\n",
       "      <td>2207.747</td>\n",
       "      <td>4138.981</td>\n",
       "      <td>2322.876</td>\n",
       "      <td>1526.459</td>\n",
       "      <td>643.457</td>\n",
       "      <td>825.010</td>\n",
       "      <td>9451.872</td>\n",
       "      <td>8499.019</td>\n",
       "      <td>9246.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9312.309</td>\n",
       "      <td>8863.707</td>\n",
       "      <td>8682.364</td>\n",
       "      <td>8800.065</td>\n",
       "      <td>8189.673</td>\n",
       "      <td>8500.773</td>\n",
       "      <td>8411.946</td>\n",
       "      <td>8684.280</td>\n",
       "      <td>9105.177</td>\n",
       "      <td>8310.093</td>\n",
       "      <td>...</td>\n",
       "      <td>2635.281</td>\n",
       "      <td>2068.886</td>\n",
       "      <td>3090.246</td>\n",
       "      <td>2306.563</td>\n",
       "      <td>1434.268</td>\n",
       "      <td>640.277</td>\n",
       "      <td>796.277</td>\n",
       "      <td>7852.998</td>\n",
       "      <td>7131.789</td>\n",
       "      <td>7689.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    美的暖通-2017年6月14日  美的暖通-2017年6月15日  美的暖通-2017年6月7日  美的暖通-2017年6月6日  \\\n",
       "0          6976.522         6664.072        6847.061        6535.154   \n",
       "1          7382.356         6729.576        6959.704        6452.257   \n",
       "2          7304.137         6776.232        6962.575        6461.012   \n",
       "3          7208.559         6627.644        6743.434        6274.364   \n",
       "4          6976.657         6473.433        6625.765        6219.593   \n",
       "5          7165.116         6685.203        6748.543        6384.397   \n",
       "6          7172.936         6750.953        6597.017        6526.251   \n",
       "7          8255.404         7761.988        7729.831        7747.975   \n",
       "8         10654.448        10306.798       10160.571       10579.151   \n",
       "9         12505.901        11582.937       11950.297       12354.701   \n",
       "10        12325.646        12117.087       12491.300       12769.085   \n",
       "11        11539.733        11399.647       11952.849       11908.215   \n",
       "12        10451.603        10294.923       10511.052       10712.121   \n",
       "13        12270.559        12293.241       12706.397       12920.156   \n",
       "14        12208.224        12471.940       13064.172       12976.776   \n",
       "15        12142.892        12634.294       12698.815       13110.601   \n",
       "16        12577.511        13141.527       13059.479       13335.454   \n",
       "17        12044.568        12590.881       12390.073       12327.019   \n",
       "18        12119.850        12672.084       12134.449       12020.669   \n",
       "19        11401.189        11933.072       11589.519       11762.121   \n",
       "20        11445.381        11681.599       10922.517       11534.700   \n",
       "21        10779.817        10997.511       10278.434       10177.559   \n",
       "22        10386.821        10497.034        9947.927        9821.216   \n",
       "23         9312.309         8863.707        8682.364        8800.065   \n",
       "24            5.000            5.000           5.000           5.000   \n",
       "\n",
       "    美的暖通-2017年6月16日  美的暖通-2017年6月8日  美的暖通-2017年6月27日  美的暖通-2017年6月22日  \\\n",
       "0          7192.544        6914.148         6521.641         6441.265   \n",
       "1          7490.105        7171.761         6753.900         6822.256   \n",
       "2          7272.688        6957.120         6575.627         6898.243   \n",
       "3          6948.480        6614.329         6255.873         6568.138   \n",
       "4          6952.443        6655.436         6281.834         6639.016   \n",
       "5          7109.747        6736.563         6322.172         6852.707   \n",
       "6          7061.544        6765.006         6368.213         7021.163   \n",
       "7          8295.015        8148.652         7659.910         8111.137   \n",
       "8         10829.080       10404.080        10044.926        10563.200   \n",
       "9         12254.890       11975.208        12315.033        12130.294   \n",
       "10        12715.611       12554.487        12929.438        12312.313   \n",
       "11        11742.651       11882.280        12254.733        10728.630   \n",
       "12        10344.623       10388.407        11159.564         9902.748   \n",
       "13        12131.025       12472.169        12843.822        11832.575   \n",
       "14        12117.560       12459.924        13042.686        12002.991   \n",
       "15        12037.679       12154.123        12626.411        11694.314   \n",
       "16        12629.166       12312.401        12235.634        11921.555   \n",
       "17        12713.449       11927.375        11693.960        12013.351   \n",
       "18        12178.015       11943.591        11289.855        12035.031   \n",
       "19        10867.836       11159.813        10804.440        11329.140   \n",
       "20        10362.015       10648.387        10953.620        11387.052   \n",
       "21         9750.716        9828.431        10198.716        10582.761   \n",
       "22         9647.695        9645.835         9827.152        10029.276   \n",
       "23         8189.673        8500.773         8411.946         8684.280   \n",
       "24            5.000           5.000            5.000            5.000   \n",
       "\n",
       "    美的暖通-2017年6月13日  美的暖通-2017年6月9日  ...  美的暖通-2017年4月29日  美的暖通-2017年7月9日  \\\n",
       "0          6454.352        6942.467  ...         4429.792        5183.657   \n",
       "1          6777.852        6997.808  ...         4179.673        5218.813   \n",
       "2          6839.785        6941.456  ...         4201.852        5162.541   \n",
       "3          6625.684        6761.413  ...         4004.179        5082.995   \n",
       "4          6664.170        6760.006  ...         3927.068        4704.864   \n",
       "5          6724.590        7069.623  ...         4054.001        4881.182   \n",
       "6          6689.710        6906.079  ...         4027.502        5261.654   \n",
       "7          7836.732        8110.130  ...         4813.448        4664.768   \n",
       "8         10123.943       10369.407  ...         5739.037        5742.621   \n",
       "9         11497.093       11897.409  ...         6075.732        6611.725   \n",
       "10        11952.754       12055.994  ...         6066.967        7109.823   \n",
       "11        11001.187       11273.673  ...         5376.436        6841.625   \n",
       "12         9524.399        9875.801  ...         4180.133        6805.765   \n",
       "13        11594.373       12189.511  ...         6147.829        6499.933   \n",
       "14        11843.340       12291.267  ...         6478.068        6042.089   \n",
       "15        12171.221       12012.051  ...         5660.681        4906.275   \n",
       "16        12469.041       11973.495  ...         5365.868        2729.154   \n",
       "17        12243.295       11375.185  ...         4432.568        2581.064   \n",
       "18        12188.242       11137.543  ...         4215.878        2300.840   \n",
       "19        11464.464       10649.232  ...         4403.736        2115.748   \n",
       "20        11138.928       10684.899  ...         4285.840        2200.870   \n",
       "21        10775.795        9927.462  ...         4157.538        2195.671   \n",
       "22        10401.284        9482.101  ...         3168.320        2207.747   \n",
       "23         9105.177        8310.093  ...         2635.281        2068.886   \n",
       "24            5.000           5.000  ...            5.000           5.000   \n",
       "\n",
       "    美的暖通-2017年8月11日  美的暖通-2017年5月30日  美的暖通-2017年8月10日  美的暖通-2017年8月8日  \\\n",
       "0          1413.178         3795.009          827.472        2284.320   \n",
       "1          1462.903         3768.801          793.855        2213.339   \n",
       "2          1487.303         3703.269          791.380        2227.660   \n",
       "3          1364.288         3528.967          767.345        2195.089   \n",
       "4          1439.261         3436.389          789.136        1978.386   \n",
       "5          1471.840         3598.205          772.941        1112.934   \n",
       "6          1460.223         3477.131          816.409         951.112   \n",
       "7          1695.763         2831.472         1151.189        1030.148   \n",
       "8          3957.502         2474.321         2099.421         665.120   \n",
       "9          6196.336         2347.460         2433.026         685.623   \n",
       "10         7335.243         2352.470         2389.656         701.716   \n",
       "11         7556.609         2318.565         2047.149         709.346   \n",
       "12         7035.952         2266.111         1816.291         794.730   \n",
       "13         7578.960         2333.731         2119.952         773.734   \n",
       "14         7965.701         2372.833         2261.507        1006.973   \n",
       "15         7438.041         2352.779         2188.487         869.103   \n",
       "16         6584.592         2366.054         2151.994         762.896   \n",
       "17         6139.991         2264.116         2175.409        1033.207   \n",
       "18         5696.883         2016.541         1899.929         794.576   \n",
       "19         5487.915         2181.770         1550.609         712.864   \n",
       "20         5337.982         2328.958         1535.863         681.093   \n",
       "21         4755.183         2353.460         1524.923         649.588   \n",
       "22         4138.981         2322.876         1526.459         643.457   \n",
       "23         3090.246         2306.563         1434.268         640.277   \n",
       "24            5.000            5.000            5.000           5.000   \n",
       "\n",
       "    美的暖通-2017年8月9日  美的暖通-2017年7月24日  美的暖通-2017年7月17日  美的暖通-2017年7月10日  \n",
       "0          706.861         3087.511         3310.951         2018.193  \n",
       "1          669.093         3308.087         3423.029         2117.549  \n",
       "2          621.136         3248.676         3377.778         2164.903  \n",
       "3          613.400         3103.760         3345.465         2147.976  \n",
       "4          623.375         3059.920         3304.632         2094.882  \n",
       "5          607.977         3281.080         3477.068         2170.454  \n",
       "6          613.499         3249.281         3457.824         2236.155  \n",
       "7          699.139         4780.648         4941.299         4379.927  \n",
       "8         1050.896         8401.331         7196.496         8210.892  \n",
       "9         1025.528        11605.523        10375.924        10666.137  \n",
       "10         966.545        12161.561        11403.978        11172.628  \n",
       "11        1082.216        11489.484        10938.689        10767.027  \n",
       "12         954.904        10112.750         9467.324         9414.349  \n",
       "13         980.348        11811.316        11181.652        10965.202  \n",
       "14        1140.508        11629.996        11458.112        11205.646  \n",
       "15        1244.219        11668.462        11407.769        11096.200  \n",
       "16        1324.763        12007.447        11404.577        11486.188  \n",
       "17        1198.379        11574.163        11152.120        11342.713  \n",
       "18        1021.604        11625.599        10772.551        10978.933  \n",
       "19         855.756        11262.184         9913.405        10235.059  \n",
       "20         851.064        11203.038         9975.845        10128.441  \n",
       "21         816.625         9809.002         9251.399         9560.675  \n",
       "22         825.010         9451.872         8499.019         9246.927  \n",
       "23         796.277         7852.998         7131.789         7689.672  \n",
       "24           5.000           10.000           10.000           10.000  \n",
       "\n",
       "[25 rows x 115 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "df_total_simple = pd.DataFrame()\n",
    "meidi_data = pd.read_csv(r'E:\\中国软件杯2022\\分类数据美的.csv',encoding='gbk')\n",
    "date_lst = meidi_data.iloc[:,0]\n",
    "for j in range(len(date_lst)):\n",
    "    temp_class = meidi_data.iloc[j,8]\n",
    "    temp_date_str = date_lst[j].replace('年','-')\n",
    "    temp_date_str = temp_date_str.replace('月','-')\n",
    "    temp_date_str = temp_date_str.replace('日','')\n",
    "    year,month,day = temp_date_str.split('-')\n",
    "    for k in time_data_lst:\n",
    "        try:\n",
    "            temp_date_name = re.search(fr'(.*{year}年[0]?{month}月[0]?{day}日.*)',k).group(1)\n",
    "        except:\n",
    "            continue\n",
    "    temp_data_every_day = pd.read_excel(fr'{temp_date_name}')\n",
    "    temp_power_data = temp_data_every_day.iloc[0:24,2].tolist()\n",
    "    temp_power_data.append(temp_class)\n",
    "    temp_one_row = pd.DataFrame(temp_power_data,columns=[f'美的暖通-{date_lst[j]}'])\n",
    "    df_total_simple = pd.concat([df_total_simple,temp_one_row],axis=1)\n",
    "df_total_simple"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab885b1",
   "metadata": {},
   "source": [
    "### 3.5 合并两次数据列，得到最终数据列Classfied_result.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b93b48a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(r'E:\\中国软件杯2022\\result.csv')\n",
    "lst = [df_total_simple,df1]\n",
    "result = pd.concat(lst,axis=1)\n",
    "result.to_csv(r'E:\\中国软件杯2022\\Classfied_result.csv',encoding='utf-8',index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55e6a32",
   "metadata": {},
   "source": [
    "## 4 选用分类器对数据进行分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ed07d4",
   "metadata": {},
   "source": [
    "### 4.1 导入数据，并对数据进行划分，得到训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a829b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "from sklearn import metrics\n",
    "from pyts.transformation import ShapeletTransform\n",
    "from pyts.datasets import load_gunpoint\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.svm import LinearSVC\n",
    "from pyts.transformation import ShapeletTransform\n",
    "import random\n",
    "\n",
    "df = pd.read_csv(r'E:\\中国软件杯2022\\Classfied_result.csv',encoding='gbk')\n",
    "train_length = 1000 #修改\n",
    "index_list = random.sample(range(0, len(df.T)), train_length)\n",
    "X_train = []\n",
    "Y_train = []\n",
    "X_test = []\n",
    "Y_test = []\n",
    "for i in range(len(df.T)):\n",
    "    if i in index_list:\n",
    "        X_train.append(df.T.iloc[i,0:24].tolist())\n",
    "        Y_train.append(df.T.iloc[i,24])\n",
    "    else:\n",
    "        X_test.append(df.T.iloc[i,0:24].tolist())\n",
    "        Y_test.append(df.T.iloc[i,24])\n",
    "Y_train = [int(i) for i in Y_train]\n",
    "Y_test = [int(i) for i in Y_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d31dbc1",
   "metadata": {},
   "source": [
    "### 4.2 对数据进行Shapelet转化，对转换后的数据进行分类，下面尝试使用了众多分类器，选择最优的三个分类器进行参数优化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97f73454",
   "metadata": {},
   "outputs": [],
   "source": [
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "# 随机森林\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc_model=RandomForestClassifier()\n",
    "clf = make_pipeline(shapelet, rfc_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score) \n",
    "\n",
    "# ET\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "et_model=ExtraTreesClassifier()\n",
    "clf = make_pipeline(shapelet, et_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score) \n",
    "\n",
    "# 朴素贝叶斯\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb_model=GaussianNB()\n",
    "clf = make_pipeline(shapelet, gnb_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score) \n",
    "\n",
    "#K最近邻\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_model=KNeighborsClassifier()\n",
    "clf = make_pipeline(shapelet, knn_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score) \n",
    "\n",
    "#逻辑回归\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_model=LogisticRegression()\n",
    "clf = make_pipeline(shapelet, lr_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score) \n",
    "\n",
    "#决策树\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt_model=DecisionTreeClassifier()\n",
    "clf = make_pipeline(shapelet, dt_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "016b0631",
   "metadata": {},
   "source": [
    "## 5 根据分类器得分，选择出随机森林、EM和K近邻分类器，下面对分类器进行参数优化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73833837",
   "metadata": {},
   "source": [
    "## 5.1 优化随机森林参数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c42763b",
   "metadata": {},
   "source": [
    "### 5.1.1 优化随机森林的n_estimators参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b72601f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "T1 = time.time()\n",
    "score2 = []\n",
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "for i in range(30,45):\n",
    "    rfc = RandomForestClassifier(n_estimators=i, n_jobs=-1, random_state=90)\n",
    "    clf = make_pipeline(shapelet, rfc)\n",
    "    clf.fit(X_train, Y_train)\n",
    "    score = clf.score(X_test, Y_test)\n",
    "    print(f'参数n_estimators为{i}的情况下，随机森林模型得分为{score}')\n",
    "    score2.append(score) \n",
    "print(max(score2),([*range(30,45)][score2.index(max(score2))])) \n",
    "plt.figure(figsize=[20,5]) \n",
    "plt.plot(range(30,45),score2) \n",
    "plt.show()\n",
    "T2 = time.time()\n",
    "print('程序运行时间:%s秒' % ((T2 - T1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62608f81",
   "metadata": {},
   "source": [
    "### 5.1.2 优化随机森林的max_features参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88f455ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "T1 = time.time()\n",
    "score2 = []\n",
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "for i in range(5,15):\n",
    "    rfc = RandomForestClassifier(n_estimators=36,max_depth=4,max_features=i, random_state=90)\n",
    "    clf = make_pipeline(shapelet, rfc)\n",
    "    clf.fit(X_train, Y_train)\n",
    "    score = clf.score(X_test, Y_test)\n",
    "    print(f'参数max_features为{i}的情况下，随机森林模型得分为{score}')\n",
    "    score2.append(score) \n",
    "print(max(score2),([*range(5,15)][score2.index(max(score2))])) \n",
    "plt.figure(figsize=[20,5]) \n",
    "plt.plot(range(5,15),score2) \n",
    "plt.show()\n",
    "T2 = time.time()\n",
    "print('程序运行时间:%s秒' % ((T2 - T1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf094c67",
   "metadata": {},
   "source": [
    "### 5.1.3 优化随机森林的min_samples_split参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7aca728f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "T1 = time.time()\n",
    "score2 = []\n",
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "for i in range(2,22):\n",
    "    rfc = RandomForestClassifier(n_estimators=36,max_depth=4, random_state=90,max_features=14,min_samples_split=i)\n",
    "    clf = make_pipeline(shapelet, rfc)\n",
    "    clf.fit(X_train, Y_train)\n",
    "    score = clf.score(X_test, Y_test)\n",
    "    print(f'参数min_samples_split为{i}的情况下，随机森林模型得分为{score}')\n",
    "    score2.append(score) \n",
    "print(max(score2),([*range(2,22)][score2.index(max(score2))])) \n",
    "plt.figure(figsize=[20,5]) \n",
    "plt.plot(range(2,22),score2) \n",
    "plt.show()\n",
    "T2 = time.time()\n",
    "print('程序运行时间:%s秒' % ((T2 - T1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd4a619",
   "metadata": {},
   "source": [
    "### 5.1.4 优化随机森林的min_samples_leaf参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72a38507",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "T1 = time.time()\n",
    "score2 = []\n",
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "for i in range(1,11):\n",
    "    rfc = RandomForestClassifier(n_estimators=36,max_depth=4, random_state=90,max_features=14,min_samples_leaf=i)\n",
    "    clf = make_pipeline(shapelet, rfc)\n",
    "    clf.fit(X_train, Y_train)\n",
    "    score = clf.score(X_test, Y_test)\n",
    "    print(f'参数min_samples_leaf为{i}的情况下，随机森林模型得分为{score}')\n",
    "    score2.append(score) \n",
    "print(max(score2),([*range(1,11)][score2.index(max(score2))])) \n",
    "plt.figure(figsize=[20,5]) \n",
    "plt.plot(range(1,11),score2) \n",
    "plt.show()\n",
    "T2 = time.time()\n",
    "print('程序运行时间:%s秒' % ((T2 - T1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c2daec",
   "metadata": {},
   "source": [
    "### 5.1.5 优化随机森林的criterion参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b9ab317",
   "metadata": {},
   "outputs": [],
   "source": [
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "rfc = RandomForestClassifier(n_estimators=36,max_depth=4, random_state=90,max_features=14,criterion='gini')\n",
    "clf = make_pipeline(shapelet, rfc)\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(f'参数criterion为gini的情况下，随机森林模型得分为{score}')\n",
    "rfc = RandomForestClassifier(n_estimators=36,max_depth=4, random_state=90,max_features=14,criterion='entropy')\n",
    "clf = make_pipeline(shapelet, rfc)\n",
    "clf.fit(X_train, Y_train)\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(f'参数criterion为entropy的情况下，随机森林模型得分为{score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03836b34",
   "metadata": {},
   "source": [
    "###  5.1.6 得到随机森林的最优参数\n",
    "```python\n",
    "    n_estimators=36,\n",
    "    max_depth=4,\n",
    "    random_state=90,\n",
    "    max_features=14,\n",
    "    min_samples_split=3,\n",
    "    min_samples_leaf=3,\n",
    "    criterion='gini'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009426ba",
   "metadata": {},
   "source": [
    "## 5.2 优化EM模型参数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54c88fb",
   "metadata": {},
   "source": [
    "### 5.2.1 优化EM模型的max_depth参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6243de28",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1,21):\n",
    "    Et_model=ExtraTreesClassifier(n_estimators=49,\n",
    "                              criterion='gini',\n",
    "                              max_depth=i,\n",
    "                              min_samples_split=2,\n",
    "                              min_samples_leaf=1,\n",
    "                              min_weight_fraction_leaf=0.0,\n",
    "                              max_features='auto',\n",
    "                              max_leaf_nodes=None, \n",
    "                              min_impurity_decrease=0.0,  \n",
    "                              bootstrap=False, \n",
    "                              oob_score=False, \n",
    "                              n_jobs=None, \n",
    "                              random_state=None, \n",
    "                              verbose=0,\n",
    "                              warm_start=False,\n",
    "                              class_weight=None,\n",
    "                              ccp_alpha=0.0,\n",
    "                              max_samples=None)\n",
    "    clf = make_pipeline(shapelet, Et_model)\n",
    "    clf.fit(x_train, y_train)\n",
    "    print('max_depth:',i)\n",
    "    print('ET模型得分:',clf.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52af08b7",
   "metadata": {},
   "source": [
    "### 5.2.2 优化EM模型的min_samples_split参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2daca691",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_samples_splits = np.linspace(0.1, 1.0, 10, endpoint=True)\n",
    "for i in min_samples_splits:\n",
    "    Et_model=ExtraTreesClassifier(n_estimators=49,\n",
    "                              criterion='gini',\n",
    "                              max_depth=8,\n",
    "                              min_samples_split=i,\n",
    "                              min_samples_leaf=1,\n",
    "                              min_weight_fraction_leaf=0.0,\n",
    "                              max_features='auto',\n",
    "                              max_leaf_nodes=None, \n",
    "                              min_impurity_decrease=0.0,  \n",
    "                              bootstrap=False, \n",
    "                              oob_score=False, \n",
    "                              n_jobs=None, \n",
    "                              random_state=None, \n",
    "                              verbose=0,\n",
    "                              warm_start=False,\n",
    "                              class_weight=None,\n",
    "                              ccp_alpha=0.0,\n",
    "                              max_samples=None)\n",
    "    clf = make_pipeline(shapelet, Et_model)\n",
    "    clf.fit(x_train, y_train)\n",
    "    print('min_samples_split:',i)\n",
    "    print('ET模型得分:',clf.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c07cb663",
   "metadata": {},
   "source": [
    "### 5.2.3 优化EM模型的min_samples_leafs参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b0371ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_samples_leafs = np.linspace(0.1,0.5,10, endpoint=True)\n",
    "for i in min_samples_leafs:\n",
    "    Et_model=ExtraTreesClassifier(n_estimators=49,\n",
    "                              criterion='gini',\n",
    "                              max_depth=8,\n",
    "                              min_samples_split=5,\n",
    "                              min_samples_leaf=i,\n",
    "                              min_weight_fraction_leaf=0.0,\n",
    "                              max_features='auto',\n",
    "                              max_leaf_nodes=None, \n",
    "                              min_impurity_decrease=0.0,  \n",
    "                              bootstrap=False, \n",
    "                              oob_score=False, \n",
    "                              n_jobs=None, \n",
    "                              random_state=None, \n",
    "                              verbose=0,\n",
    "                              warm_start=False,\n",
    "                              class_weight=None,\n",
    "                              ccp_alpha=0.0,\n",
    "                              max_samples=None)\n",
    "    clf = make_pipeline(shapelet, Et_model)\n",
    "    clf.fit(x_train, y_train)\n",
    "    print('min_samples_leaf:',i)\n",
    "    print('ET模型得分:',clf.score(x_test, y_test))            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf66131b",
   "metadata": {},
   "source": [
    "### 5.2.4 优化EM模型的criterion参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "761fdbe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_feature=['auto','sqrt','log2']\n",
    "for i in max_feature:\n",
    "    Et_model=ExtraTreesClassifier(n_estimators=49,\n",
    "                              criterion='gini',\n",
    "                              max_depth=8,\n",
    "                              min_samples_split=5,\n",
    "                              min_samples_leaf=1,\n",
    "                              min_weight_fraction_leaf=0.0,\n",
    "                              max_features=i,\n",
    "                              max_leaf_nodes=None, \n",
    "                              min_impurity_decrease=0.0,  \n",
    "                              bootstrap=False, \n",
    "                              oob_score=False, \n",
    "                              n_jobs=None, \n",
    "                              random_state=None, \n",
    "                              verbose=0,\n",
    "                              warm_start=False,\n",
    "                              class_weight=None,\n",
    "                              ccp_alpha=0.0,\n",
    "                              max_samples=None)\n",
    "    clf = make_pipeline(shapelet, Et_model)\n",
    "    clf.fit(x_train, y_train)\n",
    "    print('max_features:',i)\n",
    "    print('ET模型得分:',clf.score(x_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d362bebc",
   "metadata": {},
   "source": [
    "### 5.2.5 得到EM模型最优参数\n",
    "```python\n",
    "    n_estimators=49,\n",
    "    criterion='gini',\n",
    "    max_depth=8,\n",
    "    min_samples_split=5\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "737e070d",
   "metadata": {},
   "source": [
    "## 5.3 优化K近邻模型参数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6541fd",
   "metadata": {},
   "source": [
    "### 5.3.1 优化K近邻模型的n_neighbors参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5247742",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "#K最近邻\n",
    "score2=[]\n",
    "T1 = time.time()\n",
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "from sklearn.model_selection import cross_val_score\n",
    "for i in range(13,17):\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=i)\n",
    "    clf4 = make_pipeline(shapelet, knn_model)\n",
    "    clf4.fit(X_train, Y_train)\n",
    "    score=clf4.score(X_test, Y_test)\n",
    "    print(f'参数n_neighbors为{i}时,，K近邻模型得分为：{score}')\n",
    "    score2.append(score)\n",
    "T2 = time.time()\n",
    "print('程序运行时间:%s秒' % ((T2 - T1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d5ab77",
   "metadata": {},
   "source": [
    "### 5.3.2 优化K近邻模型的leaf_size参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6e3034d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "#K最近邻\n",
    "score2=[]\n",
    "T1 = time.time()\n",
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "from sklearn.model_selection import cross_val_score\n",
    "for i in range(20,40):\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=13,leaf_size=i)\n",
    "    clf4 = make_pipeline(shapelet, knn_model)\n",
    "    clf4.fit(X_train, Y_train)\n",
    "    score=clf4.score(X_test, Y_test)\n",
    "    print(f'参数leaf_size为{i}时,，K近邻模型得分为：{score}')\n",
    "    score2.append(score)\n",
    "T2 = time.time()\n",
    "print('程序运行时间:%s秒' % ((T2 - T1)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f1f1a8",
   "metadata": {},
   "source": [
    "### 5.3.3 得到K近邻模型的最优参数\n",
    "```python\n",
    "    n_neighbors=13,\n",
    "    leaf_size=30\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eabf01f3",
   "metadata": {},
   "source": [
    "## 6 根据最优参数，训练并保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "431d9f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "from sklearn import metrics\n",
    "from pyts.transformation import ShapeletTransform\n",
    "from pyts.datasets import load_gunpoint\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.svm import LinearSVC\n",
    "from pyts.transformation import ShapeletTransform\n",
    "import random\n",
    "import os\n",
    "import joblib\n",
    "\n",
    "df = pd.read_csv(r'E:\\中国软件杯2022\\Classfied_result.csv',encoding='gbk')\n",
    "train_length = 1000 #修改\n",
    "index_list = random.sample(range(0, len(df.T)), train_length)\n",
    "X_train = []\n",
    "Y_train = []\n",
    "X_test = []\n",
    "Y_test = []\n",
    "for i in range(len(df.T)):\n",
    "    if i in index_list:\n",
    "        X_train.append(df.T.iloc[i,0:24].tolist())\n",
    "        Y_train.append(df.T.iloc[i,24])\n",
    "    else:\n",
    "        X_test.append(df.T.iloc[i,0:24].tolist())\n",
    "        Y_test.append(df.T.iloc[i,24])\n",
    "Y_train = [int(i) for i in Y_train]\n",
    "Y_test = [int(i) for i in Y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcc2a732",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建文件目录\n",
    "dirs = 'model'\n",
    "if not os.path.exists(dirs):\n",
    "    os.makedirs(dirs)\n",
    "\n",
    "shapelet = ShapeletTransform(n_shapelets=100, window_sizes=[3])\n",
    "\n",
    "# 随机森林\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc_model=RandomForestClassifier(n_estimators=36,\n",
    "                                max_depth=4,\n",
    "                                random_state=90,\n",
    "                                max_features=14,\n",
    "                                min_samples_split=3,\n",
    "                                min_samples_leaf=3,\n",
    "                                criterion='gini')\n",
    "clf = make_pipeline(shapelet, rfc_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "# 保存模型\n",
    "joblib.dump(clf, dirs+'/RF.mdl')\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score)\n",
    "\n",
    "# ET\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "et_model=ExtraTreesClassifier(n_estimators=49,\n",
    "                            criterion='gini',\n",
    "                            max_depth=8,\n",
    "                            min_samples_split=5)\n",
    "clf = make_pipeline(shapelet, et_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "# 保存模型\n",
    "joblib.dump(clf, dirs+'/ET.mdl')\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score) \n",
    "\n",
    "#K最近邻\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_model=KNeighborsClassifier(n_neighbors=13,\n",
    "                                leaf_size=30)\n",
    "clf = make_pipeline(shapelet, knn_model)\n",
    "clf.fit(X_train, Y_train)\n",
    "# 保存模型\n",
    "joblib.dump(clf, dirs+'/K近邻.mdl')\n",
    "score = clf.score(X_test, Y_test)\n",
    "print(\"The following score was achieved on the test set: \", score)      "
   ]
  }
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